Ten Reasons Why You Don't Always Get What You Measure: II

Although many believe that "You get what you measure," metrics-based management systems sometimes produce disappointing results. In this Part II, we look at the effects of employee behavior.

Metrics-based management holds that "You get what you measure," but the assertion is actually even stronger. Many also believe that if you aren't measuring it, you won't get it. That's why it's reasonable to investigate possible causes of disappointing performance of metrics-based management. Here's part two of a collection of reasons why metrics-based management systems can disappoint. This part emphasizes employee behavior. See Part I and Part III, for more.

The Western Electric Plant at Hawthorne, Illinois, 1925. This plant was the site of a series of experiments that purportedly demonstrated observer effects, in which the act of observation affects the system being observed. The Hawthorne experiments remain controversial, but subsequent evidence of the importance of observer effects in general is more widely accepted. Photo courtesy The Eastland Memorial Society.

People aren't bolts of cloth

When we measure a length of cloth, the cloth hardly ever tries to influence the result. But employees, consciously or not, do try to make measurements "come out right." For instance, if employees fear the consequences of departing from management's expectations, they're more likely to provide data that's consistent with their estimate of management expectations.

But this effect can be even more confounding. Employees sometimes guess wrong about what management is measuring. Their biased reports then "spin" the data in a direction consistent with their interpretations of what management is measuring, rather than spinning it with respect to what management is actually measuring. Thus, even if we figure out how to correct for "spin," we might not be correcting for the right spin.

People and organizations adapt

Whether or not you believe that measurement works, it works best at first, because repeated measurements of the same attributes have decreasing impact. Soon, the measurement becomes routine, and employees adapt their actions and responses to enable a more comfortable, familiar stance.

For instance, When we measure a length of cloth, the cloth hardly ever tries to influence the resultwhen we first start tracking "show-stopper defects," we find people working hard to fix them. But after a few cycles, people develop ways of reclassifying defects to appear less severe, or they create escape clauses, or the organization develops an "appeal procedure" for obtaining waivers. The effect of the metric soon diminishes, often after a surprisingly short useful life.

Measurements of different attributes can interact

When people notice that we're measuring two different attributes, they might try to make them both "come out right," and this sometimes leads them to contradictions. For instance, to achieve long-term goals, we might have to take actions that jeopardize short-term goals, or vice versa. Thus, the act of measuring one attribute can affect the measurement of another.

Moreover, it isn't necessary that we actually make two measurements. All that's required for contamination of the data is a belief among some employees that measurement of a second attribute might take place. Perhaps we measured it in the past, or perhaps other organizations measure it, or the "literature" suggests measuring it. Even if you announce that it won't be measured, there are those who will remain skeptical, and who assume that it will happen, "just to be safe."

Just as employees make choices that can reduce the effectiveness of measurements, so can management. We'll examine that issue in a future issue. TopNext Issue

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